在计算型函数前面加上两行:from numba import jit @jit可提速近100倍

女爷i 2023-02-18 01:43 7阅读 0赞

在自定义的计算型函数前面加上两行:

from numba import jit
    @jit

可以提速近100倍(非计算的函数反可能会降速).例:

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA_size_16_color_FFFFFF_t_70][]   ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA_size_16_color_FFFFFF_t_70 1][]

import time
    from numba import jit
    @jit #加上这句可以提速近100倍
    def foo(x,y):
    	tt=time.time()
    	s=0
    	for a in range(1,6):
    		for i in range(x,y):
    			s+=i
    	print('Time used: {} sec'.format(time.time()-tt))
    	#print(s)
    	return s
    print(foo(1,100000000))
    
    #如果是非计算问题，引入即时编译，反而导致性能下降。实际python在非计算问题上性能很好
    
    
    ---------------------------------------
    Time used: 25.1532039642334 sec
    24999999750000000
    [Finished in 26.0s]
    ---------------------------------------
      warnings.warn(errors.NumbaDeprecationWarning(msg,
    Time used: 0.1136939525604248 sec
    24999999750000000
    [Finished in 1.2s]

# 详见转:[https://blog.csdn.net/qq\_39241986/article/details/102486661][https_blog.csdn.net_qq_39241986_article_details_102486661] #

(注,原文代码直接复制运行可能会出错,本文例可以直复用,但会有警告提示)

树莓派安装numba时出错,参考[https://blog.csdn.net/T\_infinity/article/details/93317077][https_blog.csdn.net_T_infinity_article_details_93317077]

numba官方文档[http://numba.pydata.org/numba-doc/latest/user/installing.html][http_numba.pydata.org_numba-doc_latest_user_installing.html]

下载numba

git clone git://github.com/numba/numba.git

numba之前先装 [https://llvmlite.readthedocs.io/en/latest/admin-guide/install.html][https_llvmlite.readthedocs.io_en_latest_admin-guide_install.html]

[https://blog.csdn.net/weixin\_37446411/article/details/55095767?ops\_request\_misc=%257B%2522request%255Fid%2522%253A%2522159218707519724848340064%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request\_id=159218707519724848340064&biz\_id=0&utm\_medium=distribute.pc\_search\_result.none-task-blog-2~all~first\_rank\_ecpm\_v3~pc\_rank\_v2-3-55095767.first\_rank\_ecpm\_v3\_pc\_rank\_v2&utm\_term=%E6%A0%91%E8%8E%93%E6%B4%BE+numba+%E5%AE%89%E8%A3%85][https_blog.csdn.net_weixin_37446411_article_details_55095767_ops_request_misc_257B_2522request_255Fid_2522_253A_2522159218707519724848340064_2522_252C_2522scm_2522_253A_252220140713.130102334.pc_255Fall._2522_257D_request_id_159218707519724848340064_biz_id_0_utm_medium_distribute.pc_search_result.none-task-blog-2_all_first_rank_ecpm_v3_pc_rank_v2-3-55095767.first_rank_ecpm_v3_pc_rank_v2_utm_term_E6_A0_91_E8_8E_93_E6_B4_BE_numba_E5_AE_89_E8_A3_85]

所需资源:[https://releases.llvm.org/download.html\#3.9.1][https_releases.llvm.org_download.html_3.9.1]

[https://pypi.org/simple/awkward-numba/][https_pypi.org_simple_awkward-numba]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200615070211285.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200615070621669.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FobWN3dA==,size_16,color_FFFFFF,t_70
[https_blog.csdn.net_qq_39241986_article_details_102486661]: https://blog.csdn.net/qq_39241986/article/details/102486661
[https_blog.csdn.net_T_infinity_article_details_93317077]: https://blog.csdn.net/T_infinity/article/details/93317077
[http_numba.pydata.org_numba-doc_latest_user_installing.html]: http://numba.pydata.org/numba-doc/latest/user/installing.html
[https_llvmlite.readthedocs.io_en_latest_admin-guide_install.html]: https://llvmlite.readthedocs.io/en/latest/admin-guide/install.html
[https_blog.csdn.net_weixin_37446411_article_details_55095767_ops_request_misc_257B_2522request_255Fid_2522_253A_2522159218707519724848340064_2522_252C_2522scm_2522_253A_252220140713.130102334.pc_255Fall._2522_257D_request_id_159218707519724848340064_biz_id_0_utm_medium_distribute.pc_search_result.none-task-blog-2_all_first_rank_ecpm_v3_pc_rank_v2-3-55095767.first_rank_ecpm_v3_pc_rank_v2_utm_term_E6_A0_91_E8_8E_93_E6_B4_BE_numba_E5_AE_89_E8_A3_85]: https://blog.csdn.net/weixin_37446411/article/details/55095767?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159218707519724848340064%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=159218707519724848340064&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v3~pc_rank_v2-3-55095767.first_rank_ecpm_v3_pc_rank_v2&utm_term=%E6%A0%91%E8%8E%93%E6%B4%BE+numba+%E5%AE%89%E8%A3%85
[https_releases.llvm.org_download.html_3.9.1]: https://releases.llvm.org/download.html#3.9.1
[https_pypi.org_simple_awkward-numba]: https://pypi.org/simple/awkward-numba/